Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations41.176
Missing cells80.837
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.6 MiB
Average record size in memory829.1 B

Variable types

Numeric14
Categorical18
Unsupported1
Boolean1

Alerts

age is highly overall correlated with age_groupHigh correlation
age_group is highly overall correlated with ageHigh correlation
campaign is highly overall correlated with costeHigh correlation
cons.conf.idx is highly overall correlated with month and 1 other fieldsHigh correlation
cons.price.idx is highly overall correlated with contact and 4 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 6 other fieldsHigh correlation
contestada is highly overall correlated with pdaysHigh correlation
coste is highly overall correlated with campaignHigh correlation
decile_score is highly overall correlated with cons.price.idx and 5 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 6 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 4 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
ingreso is highly overall correlated with roi and 3 other fieldsHigh correlation
llamar_econ is highly overall correlated with pdaysHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 7 other fieldsHigh correlation
month_num is highly overall correlated with contact and 3 other fieldsHigh correlation
nr.employed is highly overall correlated with contact and 6 other fieldsHigh correlation
pdays is highly overall correlated with contestada and 3 other fieldsHigh correlation
poutcome is highly overall correlated with previousHigh correlation
previous is highly overall correlated with poutcomeHigh correlation
roi is highly overall correlated with ingreso and 3 other fieldsHigh correlation
score_oof is highly overall correlated with contact and 7 other fieldsHigh correlation
segmento_oof is highly overall correlated with cons.conf.idx and 10 other fieldsHigh correlation
y is highly overall correlated with ingreso and 3 other fieldsHigh correlation
y_num is highly overall correlated with ingreso and 3 other fieldsHigh correlation
y_texto is highly overall correlated with ingreso and 3 other fieldsHigh correlation
default is highly imbalanced (53.3%) Imbalance
loan is highly imbalanced (51.3%) Imbalance
poutcome is highly imbalanced (56.8%) Imbalance
llamar_econ is highly imbalanced (92.8%) Imbalance
contestada is highly imbalanced (99.9%) Imbalance
pdays has 39661 (96.3%) missing values Missing
y_numeric has 41176 (100.0%) missing values Missing
decile_score is uniformly distributed Uniform
y_numeric is an unsupported type, check if it needs cleaning or further analysis Unsupported
previous has 35551 (86.3%) zeros Zeros

Reproduction

Analysis started2025-09-02 13:58:46.342195
Analysis finished2025-09-02 13:59:09.101082
Duration22.76 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.0238
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:09.195262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42068
Coefficient of variation (CV)0.26036208
Kurtosis0.79111332
Mean40.0238
Median Absolute Deviation (MAD)7
Skewness0.78456026
Sum1648020
Variance108.59057
MonotonicityNot monotonic
2025-09-02T15:59:09.333491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1947
 
4.7%
32 1845
 
4.5%
33 1833
 
4.5%
36 1779
 
4.3%
35 1758
 
4.3%
34 1745
 
4.2%
30 1714
 
4.2%
37 1475
 
3.6%
29 1453
 
3.5%
39 1430
 
3.5%
Other values (68) 24197
58.8%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 28
 
0.1%
19 42
 
0.1%
20 65
 
0.2%
21 102
 
0.2%
22 137
 
0.3%
23 226
 
0.5%
24 462
1.1%
25 598
1.5%
26 698
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 22
0.1%
87 1
 
< 0.1%
86 8
 
< 0.1%
85 15
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
admin.
10419 
blue-collar
9253 
technician
6739 
services
3967 
management
2924 
Other values (7)
7874 

Length

Max length13
Median length12
Mean length8.9554352
Min length6

Characters and Unicode

Total characters368.749
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 10419
25.3%
blue-collar 9253
22.5%
technician 6739
16.4%
services 3967
 
9.6%
management 2924
 
7.1%
retired 1718
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Length

2025-09-02T15:59:09.448382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 10419
25.3%
blue-collar 9253
22.5%
technician 6739
16.4%
services 3967
 
9.6%
management 2924
 
7.1%
retired 1718
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 47260
12.8%
n 35536
 
9.6%
a 33319
 
9.0%
l 31615
 
8.6%
i 30642
 
8.3%
c 26698
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16507
 
4.5%
t 14587
 
4.0%
Other values (14) 91799
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 368749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 47260
12.8%
n 35536
 
9.6%
a 33319
 
9.0%
l 31615
 
8.6%
i 30642
 
8.3%
c 26698
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16507
 
4.5%
t 14587
 
4.0%
Other values (14) 91799
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 368749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 47260
12.8%
n 35536
 
9.6%
a 33319
 
9.0%
l 31615
 
8.6%
i 30642
 
8.3%
c 26698
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16507
 
4.5%
t 14587
 
4.0%
Other values (14) 91799
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 368749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 47260
12.8%
n 35536
 
9.6%
a 33319
 
9.0%
l 31615
 
8.6%
i 30642
 
8.3%
c 26698
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16507
 
4.5%
t 14587
 
4.0%
Other values (14) 91799
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
married
24921 
single
11564 
divorced
4611 
unknown
 
80

Length

Max length8
Median length7
Mean length6.8311395
Min length6

Characters and Unicode

Total characters281.279
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 24921
60.5%
single 11564
28.1%
divorced 4611
 
11.2%
unknown 80
 
0.2%

Length

2025-09-02T15:59:09.548217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:09.643297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
married 24921
60.5%
single 11564
28.1%
divorced 4611
 
11.2%
unknown 80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 54453
19.4%
i 41096
14.6%
e 41096
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11804
 
4.2%
s 11564
 
4.1%
g 11564
 
4.1%
l 11564
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 54453
19.4%
i 41096
14.6%
e 41096
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11804
 
4.2%
s 11564
 
4.1%
g 11564
 
4.1%
l 11564
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 54453
19.4%
i 41096
14.6%
e 41096
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11804
 
4.2%
s 11564
 
4.1%
g 11564
 
4.1%
l 11564
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 54453
19.4%
i 41096
14.6%
e 41096
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11804
 
4.2%
s 11564
 
4.1%
g 11564
 
4.1%
l 11564
 
4.1%
Other values (6) 14153
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
university.degree
12164 
high.school
9512 
basic.9y
6045 
professional.course
5240 
basic.4y
4176 
Other values (3)
4039 

Length

Max length19
Median length17
Mean length12.710462
Min length7

Characters and Unicode

Total characters523.366
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree 12164
29.5%
high.school 9512
23.1%
basic.9y 6045
14.7%
professional.course 5240
12.7%
basic.4y 4176
 
10.1%
basic.6y 2291
 
5.6%
unknown 1730
 
4.2%
illiterate 18
 
< 0.1%

Length

2025-09-02T15:59:09.758823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:09.869382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 12164
29.5%
high.school 9512
23.1%
basic.9y 6045
14.7%
professional.course 5240
12.7%
basic.4y 4176
 
10.1%
basic.6y 2291
 
5.6%
unknown 1730
 
4.2%
illiterate 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 59172
 
11.3%
i 51628
 
9.9%
s 49908
 
9.5%
. 39428
 
7.5%
o 36474
 
7.0%
r 34826
 
6.7%
h 28536
 
5.5%
c 27264
 
5.2%
y 24676
 
4.7%
n 22594
 
4.3%
Other values (15) 148860
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 523366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 59172
 
11.3%
i 51628
 
9.9%
s 49908
 
9.5%
. 39428
 
7.5%
o 36474
 
7.0%
r 34826
 
6.7%
h 28536
 
5.5%
c 27264
 
5.2%
y 24676
 
4.7%
n 22594
 
4.3%
Other values (15) 148860
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 523366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 59172
 
11.3%
i 51628
 
9.9%
s 49908
 
9.5%
. 39428
 
7.5%
o 36474
 
7.0%
r 34826
 
6.7%
h 28536
 
5.5%
c 27264
 
5.2%
y 24676
 
4.7%
n 22594
 
4.3%
Other values (15) 148860
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 523366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 59172
 
11.3%
i 51628
 
9.9%
s 49908
 
9.5%
. 39428
 
7.5%
o 36474
 
7.0%
r 34826
 
6.7%
h 28536
 
5.5%
c 27264
 
5.2%
y 24676
 
4.7%
n 22594
 
4.3%
Other values (15) 148860
28.4%

default
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
no
32577 
unknown
8596 
yes
 
3

Length

Max length7
Median length2
Mean length3.0438848
Min length2

Characters and Unicode

Total characters125.335
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 32577
79.1%
unknown 8596
 
20.9%
yes 3
 
< 0.1%

Length

2025-09-02T15:59:09.980525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.059442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 32577
79.1%
unknown 8596
 
20.9%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 58365
46.6%
o 41173
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 58365
46.6%
o 41173
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 58365
46.6%
o 41173
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 58365
46.6%
o 41173
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
yes
21571 
no
18615 
unknown
 
990

Length

Max length7
Median length3
Mean length2.6440888
Min length2

Characters and Unicode

Total characters108.873
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 21571
52.4%
no 18615
45.2%
unknown 990
 
2.4%

Length

2025-09-02T15:59:10.146022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.235983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
yes 21571
52.4%
no 18615
45.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 21585
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19605
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 21585
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19605
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 21585
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19605
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 21585
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19605
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

loan
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
no
33938 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.2719545
Min length2

Characters and Unicode

Total characters93.550
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 33938
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Length

2025-09-02T15:59:10.331475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.417839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 33938
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 36908
39.5%
o 34928
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 36908
39.5%
o 34928
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 36908
39.5%
o 34928
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 36908
39.5%
o 34928
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

contact
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
cellular
26135 
telephone
15041 

Length

Max length9
Median length8
Mean length8.3652856
Min length8

Characters and Unicode

Total characters344.449
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 26135
63.5%
telephone 15041
36.5%

Length

2025-09-02T15:59:10.525260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.602555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
cellular 26135
63.5%
telephone 15041
36.5%

Most occurring characters

ValueCountFrequency (%)
l 93446
27.1%
e 71258
20.7%
c 26135
 
7.6%
u 26135
 
7.6%
a 26135
 
7.6%
r 26135
 
7.6%
t 15041
 
4.4%
p 15041
 
4.4%
h 15041
 
4.4%
o 15041
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 93446
27.1%
e 71258
20.7%
c 26135
 
7.6%
u 26135
 
7.6%
a 26135
 
7.6%
r 26135
 
7.6%
t 15041
 
4.4%
p 15041
 
4.4%
h 15041
 
4.4%
o 15041
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 93446
27.1%
e 71258
20.7%
c 26135
 
7.6%
u 26135
 
7.6%
a 26135
 
7.6%
r 26135
 
7.6%
t 15041
 
4.4%
p 15041
 
4.4%
h 15041
 
4.4%
o 15041
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 93446
27.1%
e 71258
20.7%
c 26135
 
7.6%
u 26135
 
7.6%
a 26135
 
7.6%
r 26135
 
7.6%
t 15041
 
4.4%
p 15041
 
4.4%
h 15041
 
4.4%
o 15041
 
4.4%

month
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
may
13767 
jul
7169 
aug
6176 
jun
5318 
nov
4100 
Other values (5)
4646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123.528
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13767
33.4%
jul 7169
17.4%
aug 6176
15.0%
jun 5318
 
12.9%
nov 4100
 
10.0%
apr 2631
 
6.4%
oct 717
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Length

2025-09-02T15:59:10.682949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.778644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
may 13767
33.4%
jul 7169
17.4%
aug 6176
15.0%
jun 5318
 
12.9%
nov 4100
 
10.0%
apr 2631
 
6.4%
oct 717
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 23120
18.7%
u 18663
15.1%
m 14313
11.6%
y 13767
11.1%
j 12487
10.1%
n 9418
7.6%
l 7169
 
5.8%
g 6176
 
5.0%
o 4817
 
3.9%
v 4100
 
3.3%
Other values (7) 9498
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23120
18.7%
u 18663
15.1%
m 14313
11.6%
y 13767
11.1%
j 12487
10.1%
n 9418
7.6%
l 7169
 
5.8%
g 6176
 
5.0%
o 4817
 
3.9%
v 4100
 
3.3%
Other values (7) 9498
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23120
18.7%
u 18663
15.1%
m 14313
11.6%
y 13767
11.1%
j 12487
10.1%
n 9418
7.6%
l 7169
 
5.8%
g 6176
 
5.0%
o 4817
 
3.9%
v 4100
 
3.3%
Other values (7) 9498
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23120
18.7%
u 18663
15.1%
m 14313
11.6%
y 13767
11.1%
j 12487
10.1%
n 9418
7.6%
l 7169
 
5.8%
g 6176
 
5.0%
o 4817
 
3.9%
v 4100
 
3.3%
Other values (7) 9498
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
thu
8618 
mon
8512 
wed
8134 
tue
8086 
fri
7826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123.528
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu 8618
20.9%
mon 8512
20.7%
wed 8134
19.8%
tue 8086
19.6%
fri 7826
19.0%

Length

2025-09-02T15:59:10.886541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:10.972933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
thu 8618
20.9%
mon 8512
20.7%
wed 8134
19.8%
tue 8086
19.6%
fri 7826
19.0%

Most occurring characters

ValueCountFrequency (%)
t 16704
13.5%
u 16704
13.5%
e 16220
13.1%
h 8618
7.0%
m 8512
6.9%
o 8512
6.9%
n 8512
6.9%
w 8134
6.6%
d 8134
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 16704
13.5%
u 16704
13.5%
e 16220
13.1%
h 8618
7.0%
m 8512
6.9%
o 8512
6.9%
n 8512
6.9%
w 8134
6.6%
d 8134
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 16704
13.5%
u 16704
13.5%
e 16220
13.1%
h 8618
7.0%
m 8512
6.9%
o 8512
6.9%
n 8512
6.9%
w 8134
6.6%
d 8134
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 16704
13.5%
u 16704
13.5%
e 16220
13.1%
h 8618
7.0%
m 8512
6.9%
o 8512
6.9%
n 8512
6.9%
w 8134
6.6%
d 8134
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

duration
Real number (ℝ)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.31582
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:11.084513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.30532
Coefficient of variation (CV)1.0038306
Kurtosis20.243771
Mean258.31582
Median Absolute Deviation (MAD)94
Skewness3.2628075
Sum10636412
Variance67239.249
MonotonicityNot monotonic
2025-09-02T15:59:11.196797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 170
 
0.4%
90 170
 
0.4%
136 168
 
0.4%
73 167
 
0.4%
124 163
 
0.4%
87 162
 
0.4%
104 161
 
0.4%
72 161
 
0.4%
111 160
 
0.4%
106 159
 
0.4%
Other values (1534) 39535
96.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 12
 
< 0.1%
5 30
 
0.1%
6 37
0.1%
7 54
0.1%
8 69
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
4199 1
< 0.1%
3785 1
< 0.1%
3643 1
< 0.1%
3631 1
< 0.1%
3509 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%

campaign
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5678793
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:11.297314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7703183
Coefficient of variation (CV)1.0788351
Kurtosis36.971857
Mean2.5678793
Median Absolute Deviation (MAD)1
Skewness4.7620441
Sum105735
Variance7.6746637
MonotonicityNot monotonic
2025-09-02T15:59:11.399282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 17634
42.8%
2 10568
25.7%
3 5340
 
13.0%
4 2650
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
Other values (32) 869
 
2.1%
ValueCountFrequency (%)
1 17634
42.8%
2 10568
25.7%
3 5340
 
13.0%
4 2650
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%

pdays
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)1.7%
Missing39661
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean6.0145215
Minimum0
Maximum27
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:11.499604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q37
95-th percentile14
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8249063
Coefficient of variation (CV)0.63594523
Kurtosis2.5645621
Mean6.0145215
Median Absolute Deviation (MAD)3
Skewness1.4585638
Sum9112
Variance14.629908
MonotonicityNot monotonic
2025-09-02T15:59:11.590257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 439
 
1.1%
6 412
 
1.0%
4 118
 
0.3%
9 64
 
0.2%
2 61
 
0.1%
7 60
 
0.1%
12 58
 
0.1%
10 52
 
0.1%
5 46
 
0.1%
13 36
 
0.1%
Other values (16) 169
 
0.4%
(Missing) 39661
96.3%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 26
 
0.1%
2 61
 
0.1%
3 439
1.1%
4 118
 
0.3%
5 46
 
0.1%
6 412
1.0%
7 60
 
0.1%
8 18
 
< 0.1%
9 64
 
0.2%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
19 3
 
< 0.1%
18 7
< 0.1%
17 8
< 0.1%
16 11
< 0.1%

previous
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17301341
Minimum0
Maximum7
Zeros35551
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:11.665836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49496438
Coefficient of variation (CV)2.8608441
Kurtosis20.102164
Mean0.17301341
Median Absolute Deviation (MAD)0
Skewness3.8313955
Sum7124
Variance0.24498974
MonotonicityNot monotonic
2025-09-02T15:59:11.757673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35551
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 35551
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 5
 
< 0.1%
5 18
 
< 0.1%
4 70
 
0.2%
3 216
 
0.5%
2 754
 
1.8%
1 4561
 
11.1%
0 35551
86.3%

poutcome
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
nonexistent
35551 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.453565
Min length7

Characters and Unicode

Total characters430.436
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 35551
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Length

2025-09-02T15:59:11.868288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:11.959233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 35551
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 106653
24.8%
e 76727
17.8%
t 71102
16.5%
i 39803
 
9.2%
s 39670
 
9.2%
x 35551
 
8.3%
o 35551
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 430436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 106653
24.8%
e 76727
17.8%
t 71102
16.5%
i 39803
 
9.2%
s 39670
 
9.2%
x 35551
 
8.3%
o 35551
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 430436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 106653
24.8%
e 76727
17.8%
t 71102
16.5%
i 39803
 
9.2%
s 39670
 
9.2%
x 35551
 
8.3%
o 35551
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 430436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 106653
24.8%
e 76727
17.8%
t 71102
16.5%
i 39803
 
9.2%
s 39670
 
9.2%
x 35551
 
8.3%
o 35551
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

emp.var.rate
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081921508
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17186
Negative (%)41.7%
Memory size1.6 MiB
2025-09-02T15:59:12.035428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5708826
Coefficient of variation (CV)19.17546
Kurtosis-1.062698
Mean0.081921508
Median Absolute Deviation (MAD)0.3
Skewness-0.72406059
Sum3373.2
Variance2.4676722
MonotonicityNot monotonic
2025-09-02T15:59:12.112066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 16228
39.4%
-1.8 9182
22.3%
1.1 7762
18.9%
-0.1 3682
 
8.9%
-2.9 1662
 
4.0%
-3.4 1070
 
2.6%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-3 172
 
0.4%
-0.2 10
 
< 0.1%
ValueCountFrequency (%)
-3.4 1070
 
2.6%
-3 172
 
0.4%
-2.9 1662
 
4.0%
-1.8 9182
22.3%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-0.2 10
 
< 0.1%
-0.1 3682
 
8.9%
1.1 7762
18.9%
1.4 16228
39.4%
ValueCountFrequency (%)
1.4 16228
39.4%
1.1 7762
18.9%
-0.1 3682
 
8.9%
-0.2 10
 
< 0.1%
-1.1 635
 
1.5%
-1.7 773
 
1.9%
-1.8 9182
22.3%
-2.9 1662
 
4.0%
-3 172
 
0.4%
-3.4 1070
 
2.6%

cons.price.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57572
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:12.191065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57883899
Coefficient of variation (CV)0.0061857818
Kurtosis-0.82985107
Mean93.57572
Median Absolute Deviation (MAD)0.38
Skewness-0.23085291
Sum3853073.8
Variance0.33505457
MonotonicityNot monotonic
2025-09-02T15:59:12.278536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 7762
18.9%
93.918 6681
16.2%
92.893 5793
14.1%
93.444 5173
12.6%
94.465 4374
10.6%
93.2 3615
8.8%
93.075 2457
 
6.0%
92.201 770
 
1.9%
92.963 715
 
1.7%
92.431 446
 
1.1%
Other values (16) 3390
8.2%
ValueCountFrequency (%)
92.201 770
 
1.9%
92.379 267
 
0.6%
92.431 446
 
1.1%
92.469 177
 
0.4%
92.649 357
 
0.9%
92.713 172
 
0.4%
92.756 10
 
< 0.1%
92.843 282
 
0.7%
92.893 5793
14.1%
92.963 715
 
1.7%
ValueCountFrequency (%)
94.767 128
 
0.3%
94.601 204
 
0.5%
94.465 4374
10.6%
94.215 311
 
0.8%
94.199 303
 
0.7%
94.055 229
 
0.6%
94.027 233
 
0.6%
93.994 7762
18.9%
93.918 6681
16.2%
93.876 212
 
0.5%

cons.conf.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.502863
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41176
Negative (%)100.0%
Memory size1.6 MiB
2025-09-02T15:59:12.361176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.62786
Coefficient of variation (CV)-0.11426007
Kurtosis-0.35909705
Mean-40.502863
Median Absolute Deviation (MAD)4.4
Skewness0.302876
Sum-1667745.9
Variance21.417088
MonotonicityNot monotonic
2025-09-02T15:59:12.450973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 7762
18.9%
-42.7 6681
16.2%
-46.2 5793
14.1%
-36.1 5173
12.6%
-41.8 4374
10.6%
-42 3615
8.8%
-47.1 2457
 
6.0%
-31.4 770
 
1.9%
-40.8 715
 
1.7%
-26.9 446
 
1.1%
Other values (16) 3390
8.2%
ValueCountFrequency (%)
-50.8 128
 
0.3%
-50 282
 
0.7%
-49.5 204
 
0.5%
-47.1 2457
 
6.0%
-46.2 5793
14.1%
-45.9 10
 
< 0.1%
-42.7 6681
16.2%
-42 3615
8.8%
-41.8 4374
10.6%
-40.8 715
 
1.7%
ValueCountFrequency (%)
-26.9 446
 
1.1%
-29.8 267
 
0.6%
-30.1 357
 
0.9%
-31.4 770
 
1.9%
-33 172
 
0.4%
-33.6 177
 
0.4%
-34.6 174
 
0.4%
-34.8 264
 
0.6%
-36.1 5173
12.6%
-36.4 7762
18.9%

euribor3m
Real number (ℝ)

High correlation 

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6212934
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:12.598890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734437
Coefficient of variation (CV)0.47895511
Kurtosis-1.4067913
Mean3.6212934
Median Absolute Deviation (MAD)0.108
Skewness-0.70919421
Sum149110.38
Variance3.0082717
MonotonicityNot monotonic
2025-09-02T15:59:12.729082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2868
 
7.0%
4.962 2611
 
6.3%
4.963 2487
 
6.0%
4.961 1902
 
4.6%
4.856 1210
 
2.9%
4.964 1175
 
2.9%
1.405 1169
 
2.8%
4.965 1070
 
2.6%
4.864 1044
 
2.5%
4.96 1013
 
2.5%
Other values (306) 24627
59.8%
ValueCountFrequency (%)
0.634 8
 
< 0.1%
0.635 43
0.1%
0.636 14
 
< 0.1%
0.637 6
 
< 0.1%
0.638 7
 
< 0.1%
0.639 16
 
< 0.1%
0.64 10
 
< 0.1%
0.642 35
0.1%
0.643 23
0.1%
0.644 38
0.1%
ValueCountFrequency (%)
5.045 9
 
< 0.1%
5 7
 
< 0.1%
4.97 172
 
0.4%
4.968 991
 
2.4%
4.967 643
 
1.6%
4.966 620
 
1.5%
4.965 1070
2.6%
4.964 1175
2.9%
4.963 2487
6.0%
4.962 2611
6.3%

nr.employed
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.0349
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:12.818244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.251364
Coefficient of variation (CV)0.013983138
Kurtosis-0.0035396701
Mean5167.0349
Median Absolute Deviation (MAD)37.1
Skewness-1.0443171
Sum2.1275783 × 108
Variance5220.2596
MonotonicityNot monotonic
2025-09-02T15:59:12.912671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 16228
39.4%
5099.1 8532
20.7%
5191 7762
18.9%
5195.8 3682
 
8.9%
5076.2 1662
 
4.0%
5017.5 1070
 
2.6%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
4963.6 635
 
1.5%
5023.5 172
 
0.4%
ValueCountFrequency (%)
4963.6 635
 
1.5%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
5017.5 1070
 
2.6%
5023.5 172
 
0.4%
5076.2 1662
 
4.0%
5099.1 8532
20.7%
5176.3 10
 
< 0.1%
5191 7762
18.9%
5195.8 3682
8.9%
ValueCountFrequency (%)
5228.1 16228
39.4%
5195.8 3682
 
8.9%
5191 7762
18.9%
5176.3 10
 
< 0.1%
5099.1 8532
20.7%
5076.2 1662
 
4.0%
5023.5 172
 
0.4%
5017.5 1070
 
2.6%
5008.7 650
 
1.6%
4991.6 773
 
1.9%

y
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
0
36537 
1
4639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41.176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Length

2025-09-02T15:59:13.004134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:13.082322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

age_group
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
25-35
13684 
35-45
13495 
45-55
8702 
55-65
3566 
<25
 
1067

Length

Max length5
Median length5
Mean length4.916019
Min length3

Characters and Unicode

Total characters202.422
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55-65
2nd row55-65
3rd row35-45
4th row35-45
5th row55-65

Common Values

ValueCountFrequency (%)
25-35 13684
33.2%
35-45 13495
32.8%
45-55 8702
21.1%
55-65 3566
 
8.7%
<25 1067
 
2.6%
65+ 662
 
1.6%

Length

2025-09-02T15:59:13.176733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:13.275996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
25-35 13684
33.2%
35-45 13495
32.8%
45-55 8702
21.1%
55-65 3566
 
8.7%
25 1067
 
2.6%
65 662
 
1.6%

Most occurring characters

ValueCountFrequency (%)
5 92891
45.9%
- 39447
19.5%
3 27179
 
13.4%
4 22197
 
11.0%
2 14751
 
7.3%
6 4228
 
2.1%
< 1067
 
0.5%
+ 662
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 92891
45.9%
- 39447
19.5%
3 27179
 
13.4%
4 22197
 
11.0%
2 14751
 
7.3%
6 4228
 
2.1%
< 1067
 
0.5%
+ 662
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 92891
45.9%
- 39447
19.5%
3 27179
 
13.4%
4 22197
 
11.0%
2 14751
 
7.3%
6 4228
 
2.1%
< 1067
 
0.5%
+ 662
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 92891
45.9%
- 39447
19.5%
3 27179
 
13.4%
4 22197
 
11.0%
2 14751
 
7.3%
6 4228
 
2.1%
< 1067
 
0.5%
+ 662
 
0.3%

y_num
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
0
36537 
1
4639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41.176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Length

2025-09-02T15:59:13.376004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:13.456646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36537
88.7%
1 4639
 
11.3%

y_numeric
Unsupported

Missing  Rejected  Unsupported 

Missing41176
Missing (%)100.0%
Memory size1.6 MiB

y_texto
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
False
36537 
True
4639 
ValueCountFrequency (%)
False 36537
88.7%
True 4639
 
11.3%
2025-09-02T15:59:13.534073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

score_oof
Real number (ℝ)

High correlation 

Distinct39813
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29546364
Minimum0.013035047
Maximum0.96419216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:13.636869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.013035047
5-th percentile0.028129862
Q10.055551785
median0.24914463
Q30.46989162
95-th percentile0.67892639
Maximum0.96419216
Range0.95115711
Interquartile range (IQR)0.41433984

Descriptive statistics

Standard deviation0.22984326
Coefficient of variation (CV)0.7779071
Kurtosis-1.0232874
Mean0.29546364
Median Absolute Deviation (MAD)0.19647356
Skewness0.52090456
Sum12166.011
Variance0.052827924
MonotonicityNot monotonic
2025-09-02T15:59:13.763549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2375217888 4
 
< 0.1%
0.03631001306 4
 
< 0.1%
0.03652274142 4
 
< 0.1%
0.6790816022 4
 
< 0.1%
0.1640505996 4
 
< 0.1%
0.03141512966 4
 
< 0.1%
0.2547007609 3
 
< 0.1%
0.07025543764 3
 
< 0.1%
0.06475311197 3
 
< 0.1%
0.03431866795 3
 
< 0.1%
Other values (39803) 41140
99.9%
ValueCountFrequency (%)
0.01303504681 1
< 0.1%
0.01356478026 1
< 0.1%
0.0136091616 1
< 0.1%
0.01382342249 1
< 0.1%
0.0138981951 1
< 0.1%
0.01393365753 1
< 0.1%
0.0139795798 1
< 0.1%
0.01413998571 1
< 0.1%
0.01417521046 1
< 0.1%
0.01434998928 1
< 0.1%
ValueCountFrequency (%)
0.9641921554 1
< 0.1%
0.953887173 1
< 0.1%
0.9493728083 1
< 0.1%
0.9485885178 1
< 0.1%
0.9477164577 1
< 0.1%
0.9471842999 1
< 0.1%
0.946206348 1
< 0.1%
0.9450128912 1
< 0.1%
0.9442207855 1
< 0.1%
0.9435692444 1
< 0.1%

segmento_oof
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Medio
24705 
Bajo
8236 
Alto
8235 

Length

Max length5
Median length5
Mean length4.5999854
Min length4

Characters and Unicode

Total characters189.409
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedio
2nd rowMedio
3rd rowMedio
4th rowMedio
5th rowMedio

Common Values

ValueCountFrequency (%)
Medio 24705
60.0%
Bajo 8236
 
20.0%
Alto 8235
 
20.0%

Length

2025-09-02T15:59:13.865493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:13.951797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
medio 24705
60.0%
bajo 8236
 
20.0%
alto 8235
 
20.0%

Most occurring characters

ValueCountFrequency (%)
o 41176
21.7%
e 24705
13.0%
M 24705
13.0%
d 24705
13.0%
i 24705
13.0%
B 8236
 
4.3%
a 8236
 
4.3%
j 8236
 
4.3%
A 8235
 
4.3%
l 8235
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 41176
21.7%
e 24705
13.0%
M 24705
13.0%
d 24705
13.0%
i 24705
13.0%
B 8236
 
4.3%
a 8236
 
4.3%
j 8236
 
4.3%
A 8235
 
4.3%
l 8235
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 41176
21.7%
e 24705
13.0%
M 24705
13.0%
d 24705
13.0%
i 24705
13.0%
B 8236
 
4.3%
a 8236
 
4.3%
j 8236
 
4.3%
A 8235
 
4.3%
l 8235
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 41176
21.7%
e 24705
13.0%
M 24705
13.0%
d 24705
13.0%
i 24705
13.0%
B 8236
 
4.3%
a 8236
 
4.3%
j 8236
 
4.3%
A 8235
 
4.3%
l 8235
 
4.3%

decile_score
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
D10
4118 
D9
4118 
D7
4118 
D5
4118 
D1
4118 
Other values (5)
20586 

Length

Max length3
Median length2
Mean length2.1000097
Min length2

Characters and Unicode

Total characters86.470
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD7
2nd rowD7
3rd rowD5
4th rowD5
5th rowD5

Common Values

ValueCountFrequency (%)
D10 4118
10.0%
D9 4118
10.0%
D7 4118
10.0%
D5 4118
10.0%
D1 4118
10.0%
D3 4118
10.0%
D6 4117
10.0%
D8 4117
10.0%
D4 4117
10.0%
D2 4117
10.0%

Length

2025-09-02T15:59:14.047828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:14.551959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
d10 4118
10.0%
d9 4118
10.0%
d7 4118
10.0%
d5 4118
10.0%
d1 4118
10.0%
d3 4118
10.0%
d6 4117
10.0%
d8 4117
10.0%
d4 4117
10.0%
d2 4117
10.0%

Most occurring characters

ValueCountFrequency (%)
D 41176
47.6%
1 8236
 
9.5%
0 4118
 
4.8%
9 4118
 
4.8%
7 4118
 
4.8%
5 4118
 
4.8%
3 4118
 
4.8%
6 4117
 
4.8%
8 4117
 
4.8%
4 4117
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 41176
47.6%
1 8236
 
9.5%
0 4118
 
4.8%
9 4118
 
4.8%
7 4118
 
4.8%
5 4118
 
4.8%
3 4118
 
4.8%
6 4117
 
4.8%
8 4117
 
4.8%
4 4117
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 41176
47.6%
1 8236
 
9.5%
0 4118
 
4.8%
9 4118
 
4.8%
7 4118
 
4.8%
5 4118
 
4.8%
3 4118
 
4.8%
6 4117
 
4.8%
8 4117
 
4.8%
4 4117
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 41176
47.6%
1 8236
 
9.5%
0 4118
 
4.8%
9 4118
 
4.8%
7 4118
 
4.8%
5 4118
 
4.8%
3 4118
 
4.8%
6 4117
 
4.8%
8 4117
 
4.8%
4 4117
 
4.8%

llamar_econ
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
1
40819 
0
 
357

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41.176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

Length

2025-09-02T15:59:14.665815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:14.745143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40819
99.1%
0 357
 
0.9%

contestada
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
1
41172 
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41.176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

Length

2025-09-02T15:59:14.831939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:14.916265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41172
> 99.9%
0 4
 
< 0.1%

coste
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.03638
Minimum30
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-09-02T15:59:15.009982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q130
median60
Q390
95-th percentile210
Maximum1680
Range1650
Interquartile range (IQR)60

Descriptive statistics

Standard deviation83.10955
Coefficient of variation (CV)1.0788351
Kurtosis36.971857
Mean77.03638
Median Absolute Deviation (MAD)30
Skewness4.7620441
Sum3172050
Variance6907.1973
MonotonicityNot monotonic
2025-09-02T15:59:15.122553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
30 17634
42.8%
60 10568
25.7%
90 5340
 
13.0%
120 2650
 
6.4%
150 1599
 
3.9%
180 979
 
2.4%
210 629
 
1.5%
240 400
 
1.0%
270 283
 
0.7%
300 225
 
0.5%
Other values (32) 869
 
2.1%
ValueCountFrequency (%)
30 17634
42.8%
60 10568
25.7%
90 5340
 
13.0%
120 2650
 
6.4%
150 1599
 
3.9%
180 979
 
2.4%
210 629
 
1.5%
240 400
 
1.0%
270 283
 
0.7%
300 225
 
0.5%
ValueCountFrequency (%)
1680 1
 
< 0.1%
1290 2
 
< 0.1%
1260 2
 
< 0.1%
1230 1
 
< 0.1%
1200 2
 
< 0.1%
1170 1
 
< 0.1%
1110 1
 
< 0.1%
1050 5
< 0.1%
1020 3
< 0.1%
990 4
< 0.1%

ingreso
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
0.0
36537 
1500.0
4639 

Length

Max length6
Median length3
Mean length3.3379881
Min length3

Characters and Unicode

Total characters137.445
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36537
88.7%
1500.0 4639
 
11.3%

Length

2025-09-02T15:59:15.229849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-02T15:59:15.317596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36537
88.7%
1500.0 4639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 86991
63.3%
. 41176
30.0%
1 4639
 
3.4%
5 4639
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86991
63.3%
. 41176
30.0%
1 4639
 
3.4%
5 4639
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86991
63.3%
. 41176
30.0%
1 4639
 
3.4%
5 4639
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86991
63.3%
. 41176
30.0%
1 4639
 
3.4%
5 4639
 
3.4%

roi
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8946934
Minimum-1
Maximum49
Zeros0
Zeros (%)0.0%
Negative36537
Negative (%)88.7%
Memory size1.6 MiB
2025-09-02T15:59:15.411022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile49
Maximum49
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.170825
Coefficient of variation (CV)4.20453
Kurtosis8.7935662
Mean2.8946934
Median Absolute Deviation (MAD)0
Skewness3.1784415
Sum119191.9
Variance148.12899
MonotonicityNot monotonic
2025-09-02T15:59:15.501069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-1 36537
88.7%
49 2299
 
5.6%
24 1211
 
2.9%
15.66666667 574
 
1.4%
11.5 249
 
0.6%
9 120
 
0.3%
7.333333333 75
 
0.2%
6.142857143 38
 
0.1%
5.25 17
 
< 0.1%
4.555555556 17
 
< 0.1%
Other values (8) 39
 
0.1%
ValueCountFrequency (%)
-1 36537
88.7%
1.173913043 1
 
< 0.1%
1.941176471 4
 
< 0.1%
2.333333333 2
 
< 0.1%
2.571428571 1
 
< 0.1%
2.846153846 4
 
< 0.1%
3.166666667 3
 
< 0.1%
3.545454545 12
 
< 0.1%
4 12
 
< 0.1%
4.555555556 17
 
< 0.1%
ValueCountFrequency (%)
49 2299
5.6%
24 1211
2.9%
15.66666667 574
 
1.4%
11.5 249
 
0.6%
9 120
 
0.3%
7.333333333 75
 
0.2%
6.142857143 38
 
0.1%
5.25 17
 
< 0.1%
4.555555556 17
 
< 0.1%
4 12
 
< 0.1%

month_num
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6077327
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-09-02T15:59:15.581627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q38
95-th percentile11
Maximum12
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.041013
Coefficient of variation (CV)0.3088825
Kurtosis-0.027627601
Mean6.6077327
Median Absolute Deviation (MAD)1
Skewness0.85168326
Sum272080
Variance4.165734
MonotonicityNot monotonic
2025-09-02T15:59:15.659532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 13767
33.4%
7 7169
17.4%
8 6176
15.0%
6 5318
 
12.9%
11 4100
 
10.0%
4 2631
 
6.4%
10 717
 
1.7%
9 570
 
1.4%
3 546
 
1.3%
12 182
 
0.4%
ValueCountFrequency (%)
3 546
 
1.3%
4 2631
 
6.4%
5 13767
33.4%
6 5318
 
12.9%
7 7169
17.4%
8 6176
15.0%
9 570
 
1.4%
10 717
 
1.7%
11 4100
 
10.0%
12 182
 
0.4%
ValueCountFrequency (%)
12 182
 
0.4%
11 4100
 
10.0%
10 717
 
1.7%
9 570
 
1.4%
8 6176
15.0%
7 7169
17.4%
6 5318
 
12.9%
5 13767
33.4%
4 2631
 
6.4%
3 546
 
1.3%

Interactions

2025-09-02T15:59:06.944447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:51.739865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.922796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.256835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.347667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.392401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.715725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.776484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.841242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.032990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.439064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.531606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.676380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.858732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.036225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:51.826793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.011303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.352693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.432786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.507031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.797064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.859133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.927293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.133583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.522670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.620199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.765703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.948933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.113487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:51.905732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.092559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.434640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.519191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.584612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.870476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.939611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.005579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.218877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.599080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.693843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.853027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.026258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.531308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:51.990311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.172034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.514016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.594292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.659896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.939918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.010138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.082507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.296297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.675270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.770930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.945688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.099671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.611637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.086471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.255471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.590921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.666439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.736455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.017596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.082876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.170507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.390563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.754549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.849088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.034371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.172951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.696578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.188061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.338932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.673404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.742191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.816901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.094907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.162283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.253005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.489048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.836955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.936648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.121624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.255935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.773358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.267696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.419195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.745921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.821691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.892575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.164537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.233356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.322448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.568552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.909718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.012884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.202034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.330576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.848411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.348956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.505701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.819996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.891523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.966400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.236523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.302131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.397681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.640448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.986792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.091232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.284404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.405041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.922310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.429016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.584296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.895216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.960442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.042776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.310970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.373393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.478725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:01.712018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.066595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.168493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.364760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.480102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:07.995999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.505211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.662805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.967649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.036727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.114947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.396941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.446511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.575432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.055490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.141598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.242736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.443103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.554392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:08.071299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.586584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.737675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.040440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.104421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.190268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.465261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.517533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.660842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.128076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.217873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.325197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.525987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.631063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:08.147917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.664761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.819746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.115116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.174059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.265861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.541487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.593664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.757326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.204392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.294659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.413603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.608455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.709054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:08.228398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.752997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:53.904233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.194420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.250891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.565976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.628349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.682532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.847558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.290283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.377068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.512710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.694285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.791232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:08.305041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:52.839032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:54.176309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:55.272938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:56.320839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:57.641812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:58.704690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:58:59.765724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:00.941704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:02.368217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:03.455690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:04.602109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:05.777230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-09-02T15:59:06.867591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-09-02T15:59:15.745860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ageage_groupcampaigncons.conf.idxcons.price.idxcontactcontestadacosteday_of_weekdecile_scoredefaultdurationeducationemp.var.rateeuribor3mhousingingresojobllamar_econloanmaritalmonthmonth_numnr.employedpdayspoutcomepreviousroiscore_oofsegmento_oofyy_numy_texto
age1.0000.8280.0060.1140.0450.0990.0000.0060.0250.1100.146-0.0020.1170.0450.0540.0000.1720.2490.0860.0100.2620.0940.0630.045-0.0560.109-0.013-0.012-0.0790.0970.1720.1720.172
age_group0.8281.0000.0000.1520.1490.1060.0000.0000.0230.1460.1470.0000.1350.1390.1620.0050.1710.3350.0950.0040.2540.1200.1200.1590.0000.1090.0700.0780.1460.0980.1710.1710.171
campaign0.0060.0001.000-0.0010.0960.0640.0001.0000.0180.0460.017-0.0810.0020.1560.1410.0220.0520.0000.0000.0210.0000.047-0.0110.1440.0590.047-0.087-0.083-0.0880.0540.0520.0520.052
cons.conf.idx0.1140.152-0.0011.0000.2460.4170.000-0.0010.0450.4130.138-0.0090.0640.2250.2370.0400.3860.1090.1320.0110.0720.6000.3480.133-0.0150.369-0.1160.042-0.3770.6450.3860.3860.386
cons.price.idx0.0450.1490.0960.2461.0000.6750.0000.0960.0500.5200.1540.0030.0980.6650.4910.0690.3360.1310.2370.0170.0690.676-0.0550.4650.1550.386-0.283-0.124-0.3650.7900.3360.3360.336
contact0.0990.1060.0640.4170.6751.0000.0000.0640.0550.7290.1360.0320.1230.4620.4690.0850.1450.1280.0710.0240.0720.6090.6090.5020.0100.2420.2420.1470.7240.5140.1450.1450.145
contestada0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
coste0.0060.0001.000-0.0010.0960.0640.0001.0000.0180.0460.017-0.0810.0020.1560.1410.0220.0520.0000.0000.0210.0000.047-0.0110.1440.0590.047-0.087-0.083-0.0880.0540.0520.0520.052
day_of_week0.0250.0230.0180.0450.0500.0550.0000.0181.0000.1800.0110.0080.0200.0350.1370.0150.0230.0160.0330.0060.0110.0670.0670.0460.0720.0150.0000.0180.1450.0670.0230.0230.023
decile_score0.1100.1460.0460.4130.5200.7290.0000.0460.1801.0000.2090.0080.0950.5310.4970.0990.3250.1140.2800.0750.1020.4740.4740.5480.3270.3400.1810.1490.7211.0000.3250.3250.325
default0.1460.1470.0170.1380.1540.1360.0000.0170.0110.2091.0000.0000.1700.1570.1590.0110.0990.1520.1770.0020.0950.1120.1120.1400.0000.0770.0750.0700.1340.1030.0990.0990.099
duration-0.0020.000-0.081-0.0090.0030.0320.000-0.0810.0080.0080.0001.0000.000-0.069-0.0780.0000.3770.0060.0000.0000.0000.020-0.057-0.0950.1020.0170.0420.3460.0560.0000.3770.3770.377
education0.1170.1350.0020.0640.0980.1230.0000.0020.0200.0950.1700.0001.0000.0660.0600.0130.0670.3590.0530.0000.1160.0950.0950.0670.0490.0420.0190.0300.0860.0750.0670.0670.067
emp.var.rate0.0450.1390.1560.2250.6650.4620.0000.1560.0350.5310.157-0.0690.0661.0000.9400.0520.3420.1350.0780.0120.0680.6590.3070.9450.1490.380-0.435-0.251-0.8200.6030.3420.3420.342
euribor3m0.0540.1620.1410.2370.4910.4690.0000.1410.1370.4970.159-0.0780.0600.9401.0000.0520.3990.1280.0780.0120.0680.5520.2910.929-0.0910.418-0.455-0.270-0.8370.6730.3990.3990.399
housing0.0000.0050.0220.0400.0690.0850.0000.0220.0150.0990.0110.0000.0130.0520.0521.0000.0100.0110.0230.7080.0090.0540.0540.0400.0240.0170.0160.0050.0900.0440.0100.0100.010
ingreso0.1720.1710.0520.3860.3360.1450.0000.0520.0230.3250.0990.3770.0670.3420.3990.0101.0000.1520.0160.0000.0540.2740.2740.4100.0980.3200.2360.9950.3340.0881.0001.0001.000
job0.2490.3350.0000.1090.1310.1280.0000.0000.0160.1140.1520.0060.3590.1350.1280.0110.1521.0000.0790.0100.1840.1100.1100.1340.0420.1000.0530.0690.1140.1140.1520.1520.152
llamar_econ0.0860.0950.0000.1320.2370.0710.0000.0000.0330.2800.1770.0000.0530.0780.0780.0230.0160.0791.0000.0180.0640.2200.2200.1151.0000.0370.0350.0150.1460.1870.0160.0160.016
loan0.0100.0040.0210.0110.0170.0240.0000.0210.0060.0750.0020.0000.0000.0120.0120.7080.0000.0100.0181.0000.0000.0200.0200.0100.0000.0000.0000.0040.0630.0190.0000.0000.000
marital0.2620.2540.0000.0720.0690.0720.0000.0000.0110.1020.0950.0000.1160.0680.0680.0090.0540.1840.0640.0001.0000.0500.0500.0720.0000.0370.0300.0320.0760.0530.0540.0540.054
month0.0940.1200.0470.6000.6760.6090.0000.0470.0670.4740.1120.0200.0950.6590.5520.0540.2740.1100.2200.0200.0501.0001.0000.6020.1770.2420.1270.1300.4590.6230.2740.2740.274
month_num0.0630.120-0.0110.348-0.0550.6090.000-0.0110.0670.4740.112-0.0570.0950.3070.2910.0540.2740.1100.2200.0200.0501.0001.0000.412-0.0300.242-0.0160.027-0.5580.6230.2740.2740.274
nr.employed0.0450.1590.1440.1330.4650.5020.0000.1440.0460.5480.140-0.0950.0670.9450.9290.0400.4100.1340.1150.0100.0720.6020.4121.000-0.1650.412-0.439-0.287-0.8250.7250.4100.4100.410
pdays-0.0560.0000.059-0.0150.1550.0101.0000.0590.0720.3270.0000.1020.0490.149-0.0910.0240.0980.0421.0000.0000.0000.177-0.030-0.1651.0000.371-0.001-0.0430.5790.6520.0980.0980.098
poutcome0.1090.1090.0470.3690.3860.2420.0000.0470.0150.3400.0770.0170.0420.3800.4180.0170.3200.1000.0370.0000.0370.2420.2420.4120.3711.0000.7340.2340.3790.2280.3200.3200.320
previous-0.0130.070-0.087-0.116-0.2830.2420.000-0.0870.0000.1810.0750.0420.019-0.435-0.4550.0160.2360.0530.0350.0000.0300.127-0.016-0.439-0.0010.7341.0000.2030.3670.2280.2360.2360.236
roi-0.0120.078-0.0830.042-0.1240.1470.000-0.0830.0180.1490.0700.3460.030-0.251-0.2700.0050.9950.0690.0150.0040.0320.1300.027-0.287-0.0430.2340.2031.0000.1830.0670.9950.9950.995
score_oof-0.0790.146-0.088-0.377-0.3650.7240.000-0.0880.1450.7210.1340.0560.086-0.820-0.8370.0900.3340.1140.1460.0630.0760.459-0.558-0.8250.5790.3790.3670.1831.0000.8880.3340.3340.334
segmento_oof0.0970.0980.0540.6450.7900.5140.0000.0540.0671.0000.1030.0000.0750.6030.6730.0440.0880.1140.1870.0190.0530.6230.6230.7250.6520.2280.2280.0670.8881.0000.0880.0880.088
y0.1720.1710.0520.3860.3360.1450.0000.0520.0230.3250.0990.3770.0670.3420.3990.0101.0000.1520.0160.0000.0540.2740.2740.4100.0980.3200.2360.9950.3340.0881.0001.0001.000
y_num0.1720.1710.0520.3860.3360.1450.0000.0520.0230.3250.0990.3770.0670.3420.3990.0101.0000.1520.0160.0000.0540.2740.2740.4100.0980.3200.2360.9950.3340.0881.0001.0001.000
y_texto0.1720.1710.0520.3860.3360.1450.0000.0520.0230.3250.0990.3770.0670.3420.3990.0101.0000.1520.0160.0000.0540.2740.2740.4100.0980.3200.2360.9950.3340.0881.0001.0001.000

Missing values

2025-09-02T15:59:08.456542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-02T15:59:08.866338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedyage_groupy_numy_numericy_textoscore_oofsegmento_oofdecile_scorellamar_econcontestadacosteingresoroimonth_num
056housemaidmarriedbasic.4ynononotelephonemaymon2611NaN0nonexistent1.193.994-36.44.8575191.0055-650NaNno0.312252MedioD71130.00.0-1.05
157servicesmarriedhigh.schoolunknownnonotelephonemaymon1491NaN0nonexistent1.193.994-36.44.8575191.0055-650NaNno0.297544MedioD71130.00.0-1.05
237servicesmarriedhigh.schoolnoyesnotelephonemaymon2261NaN0nonexistent1.193.994-36.44.8575191.0035-450NaNno0.243202MedioD51130.00.0-1.05
340admin.marriedbasic.6ynononotelephonemaymon1511NaN0nonexistent1.193.994-36.44.8575191.0035-450NaNno0.227266MedioD51130.00.0-1.05
456servicesmarriedhigh.schoolnonoyestelephonemaymon3071NaN0nonexistent1.193.994-36.44.8575191.0055-650NaNno0.243402MedioD51130.00.0-1.05
545servicesmarriedbasic.9yunknownnonotelephonemaymon1981NaN0nonexistent1.193.994-36.44.8575191.0045-550NaNno0.322168MedioD71130.00.0-1.05
659admin.marriedprofessional.coursenononotelephonemaymon1391NaN0nonexistent1.193.994-36.44.8575191.0055-650NaNno0.311275MedioD71130.00.0-1.05
741blue-collarmarriedunknownunknownnonotelephonemaymon2171NaN0nonexistent1.193.994-36.44.8575191.0035-450NaNno0.275664MedioD61130.00.0-1.05
824techniciansingleprofessional.coursenoyesnotelephonemaymon3801NaN0nonexistent1.193.994-36.44.8575191.00<250NaNno0.305777MedioD71130.00.0-1.05
925servicessinglehigh.schoolnoyesnotelephonemaymon501NaN0nonexistent1.193.994-36.44.8575191.0025-350NaNno0.245168MedioD51130.00.0-1.05
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedyage_groupy_numy_numericy_textoscore_oofsegmento_oofdecile_scorellamar_econcontestadacosteingresoroimonth_num
4117862retiredmarrieduniversity.degreenononocellularnovthu48326.03success-1.194.767-50.81.0314963.6155-651NaNyes0.449263MedioD81160.01500.024.011
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri1513NaN0nonexistent-1.194.767-50.81.0284963.6055-650NaNno0.382377MedioD71190.00.0-1.011
4118036admin.marrieduniversity.degreenononocellularnovfri2542NaN0nonexistent-1.194.767-50.81.0284963.6035-450NaNno0.375936MedioD71160.00.0-1.011
4118137admin.marrieduniversity.degreenoyesnocellularnovfri2811NaN0nonexistent-1.194.767-50.81.0284963.6135-451NaNyes0.371484MedioD71130.01500.049.011
4118229unemployedsinglebasic.4ynoyesnocellularnovfri11219.01success-1.194.767-50.81.0284963.6025-350NaNno0.477718MedioD81130.00.0-1.011
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri3341NaN0nonexistent-1.194.767-50.81.0284963.6165+1NaNyes0.371089MedioD71130.01500.049.011
4118446blue-collarmarriedprofessional.coursenononocellularnovfri3831NaN0nonexistent-1.194.767-50.81.0284963.6045-550NaNno0.370680MedioD71130.00.0-1.011
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri1892NaN0nonexistent-1.194.767-50.81.0284963.6055-650NaNno0.370407MedioD71160.00.0-1.011
4118644technicianmarriedprofessional.coursenononocellularnovfri4421NaN0nonexistent-1.194.767-50.81.0284963.6135-451NaNyes0.375753MedioD71130.01500.049.011
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri2393NaN1failure-1.194.767-50.81.0284963.6065+0NaNno0.375047MedioD71190.00.0-1.011